Purchase this article with an account.
M. Balasubramanian, C. Bowd, P. Wolenski, F. A. Medeiros, L. M. Alencar, P. A. Sample, R. N. Weinreb, L. M. Zangwill; Evaluation of a Novel Proper Orthogonal Decomposition (POD) Framework for Detecting Glaucomatous Changes in Human Subjects. Invest. Ophthalmol. Vis. Sci. 2007;48(13):3331.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
To evaluate and compare a novel POD framework to Topographic Change Analysis (TCA) for detecting progression in HRT topographs.
In POD framework, a set of basis vectors is constructed using an ensemble of optic nerve head (ONH) regions from topographs of an eye at baseline (BL). The bases capture any instrument inter/intra-session topograph variability and inherent variability in the ONH at BL. Changes in a follow-up topograph If are determined by reconstructing If using the BL bases. POD parameters a) Euclidean distance (L2 norm), b) Image Euclidean distance and c) correlation between If and its reconstruction were computed and compared to TCA parameters a) total number of super-pixels with significant decrease in retinal height (red pixels), b) size of the largest red-pixel cluster (CSIZE) and c) CSIZE % of disc area, all within the disc margin. A total of 234 eyes with ≥ 4 HRT exams with a median follow-up of 5.5 years were selected from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS) database. Standard Automated Perimetry Glaucoma Progression Analysis (GPA) was performed on all eyes using the 2 visual fields (VF) closest to the first HRT exam as baseline, and all reliable subsequent VFs as follow-up. GPA change was defined as ≥ 3 changed (p<5%) test points repeatable on ≥ 3 consecutive tests. For evaluation, HRT exams were categorized as changed and not-changed using the GPA VF change detection date.
Estimates of the Area Under ROC curve (AUC) for discriminating between changed and not-changed topographs and optimal parameter cutoffs are presented below. POD L2 norm and TCA CSIZE resulted in the largest AUC in their respective frameworks and their difference (0.03) was statistically insignificant.
The new POD framework provides a similar performance as the TCA in detecting topographic changes. An advantage of the POD framework is its ability to detect and confirm changes using a single follow-up topograph by estimating instrument/structure variability from a set of BL topographs.
This PDF is available to Subscribers Only